Prioritization of congenital cardiac surgical patients using fuzzy reasoning – a solution to the problem of the waiting list?

2006 ◽  
Vol 16 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Ralf Holzer ◽  
Ed Ladusans ◽  
Denise Kitchiner ◽  
Ian Peart ◽  
Gordon Gladman ◽  
...  
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Production System ◽  
Medical Decision Making ◽  
Waiting List ◽  
Surgical Patients ◽  
Medical Decision ◽  
Impact Factors ◽  
Decision Making Process ◽  
Rule Based

Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent.The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system (“PrioHeart”).The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate “timing category” within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category.We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base.The “PrioHeart” expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of “PrioHeart” on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using “PrioHeart” and those results obtained by the individual consultant specialist.We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Incorporating patient preferences and priorities into end-of-life care.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 41-41
Author(s):  
Eric Rackow ◽  
Afua Ofori ◽  
Wendy Rodkey ◽  
Roy A. Beveridge
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
End Of Life Care ◽  
Medical Decision Making ◽  
Case Managers ◽  
Readiness For Change ◽  
Medical Decision ◽  
Decision Making Process ◽  
Life Care

41 Background: Patients with advanced illness often face painful conversations and difficult decisions. A program was deployed to help patients identify, communicate, and incorporate their personal preferences and priorities into decisions about their care. The program was assessed by measuring movement along the readiness for change continuum. Methods: Patients residing in the home and participating in a chronic care program were referred by their case managers based on clinical conditions and whether the patient appeared to be in their last 12 months of life. Counseling sessions with patients or family caregiver/s were designed to move participants toward the following actions: be fully informed about their medical situation, describe their detailed quality of life priorities, articulate a self-defined medical decision making process, effectively communicate to their family and physicians, and implement and repeat the aforementioned steps. After 5 months (Sept-2014 to Feb-2015), movement along the readiness for change continuum (pre-contemplation, contemplation, preparation, action, maintenance, and advocacy) was reported. Results: Of the 427 patients referred, 33 could not be reached, 116 were ineligible, 50 declined or did not engage. Of the 228 participants, 191 (84%) moved at least one step in readiness for change continuum over the 5-month period. In Nov-2014, 13% of participants were in action, maintenance, or advocacy, which increased to 19% by Feb-2015. The largest observed movement to action, maintenance, or advocacy was in defining quality of life priorities: 2% Nov-2014 to 21% Feb-2015. The least movement to action, maintenance, or advocacy was observed in articulating a self-defined medical decision making process: 3% Nov-2014 to 16% Feb-2015. Case managers reported discomfort in referring members based on their assessment of length of life. Early surveys show high levels of satisfaction. Conclusions: A very high percentage of patients progressed in incorporating their preferences and priorities into end of life care as measured by the readiness to change continuum. This program is currently expanding and the referral process is changing from case manager to algorithm based identification referrals.

scholarly journals Application of a computerized medical decision-making process to the problem of digoxin intoxication

1984 ◽  
Vol 4 (3) ◽  
pp. 571-576 ◽  
Author(s):  
Keith S. White ◽  
Alan Lindsay ◽  
T. Allan Pryor ◽  
Wayne F. Brown ◽  
Kevin Walsh
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Decision Making Process ◽  
Digoxin Intoxication

scholarly journals The role of non-medical factors in physicians’ decision-making process in a pediatric telemedicine service

2019 ◽  
Vol 26 (2) ◽  
pp. 1152-1176 ◽  
Author(s):  
Motti Haimi ◽  
Shuli Brammli-Greenberg ◽  
Yehezkel Waisman ◽  
Nili Stein ◽  
Orna Baron-Epel
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Decision Making Process ◽  
Medical Decisions ◽  
Telemedicine Service ◽  
Medical Factors ◽  
Complex Process ◽  
Clinical Scenarios

The complex process of medical decision-making is prone also to medically extraneous influences or “non-medical” factors. We aimed to investigate the possible role of non-medical factors in doctors’ decision-making process in a telemedicine setting. Interviews with 15 physicians who work in a pediatric telemedicine service were conducted. Those included a qualitative section, in which the physicians were asked about the role of non-medical factors in their decisions. Their responses to three clinical scenarios were also analyzed. In an additional quantitative section, a random sample of 339 parent -physician consultations, held during 2014–2017, was analyzed retrospectively. Various non-medical factors were identified with respect to their possible effect on primary and secondary decisions, the accuracy of diagnosis, and “reasonability” of the decisions. Various non-medical factors were found to influence physicians’ decisions. Those factors were related to the child, the applying parent, the physician, the interaction between the doctor and parents, the shift, and to demographic considerations, and were also found to influence the ability to make an accurate diagnosis and “reasonable” decisions. Our conclusion was that non-medical factors have an impact on doctor’s decisions, even in the setting of telemedicine, and should be considered for improving medical decisions in this milieu.

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